Digital image zooming provides a focused field of view on one area of an image. The image may be from a digital photography or a video image. In most instances, digital zooming can be achieved by interpolating adjacent pixels of the area of the image to be zoomed in on. Image zooming techniques are typically used in several areas of image processing, including but are not limited to, photographical enlarging, image reconstruction, and reducing artifacts. These functions are valuable for image processing in consumer graphics editors, video processing, video players, medical imaging, computer graphics, defense applications, etc.
Many algorithms have been developed for enlarging images. For example, Interpolation functions can be applied to an image for resampling. Typical Interpolation algorithms used for image resampling include the nearest neighbor interpolation which is similar to a sinc function in the frequency domain, a linear function, which attenuates frequencies near the cutoff frequency, a sinc function, a radial basis function, the cubic B-spline, the high-resolution cubic spleens, and the generalized spline filters, for example.
Oftentimes, interpolation functions result in artifacts such as, edge halos, aliasing, and blurring. Therefore, adaptable interpolation techniques suitable for varying local structures of an image have been developed in an attempt to preserve the resolution at edges during interpolation. In addition, structurally adaptive techniques have also been utilized to take advantage of bandwidth and intensity variations unapparent to the human eye.
Images can be segmented into many regions to apply different interpolation functions based on the analysis of the local structure. For example, interpolation, extrapolation or pixel replication can be applied depending on the detected image structures, including a homogeneous area, edge area, or isolated feature area. However, although adaptive interpolation techniques that depend on image content are able to generate sharper images with fewer artifacts compared to that with the non-adaptive interpolation method, they are generally more complex and resource intensive.
However, most of the algorithms for image zooming do not preserve image intensity between the original image and the zoomed image. The perceived intensity by the human eye can potentially affect the perception of sharpness in the zoomed image.